Solution of ECML/PKDD 2016 Discovery Challenge Task 2, “Upselling prediction”, team “Peter”

1. A Simple yet Efficient Method for a
Credit Card Upselling Predic;on
Peter Romov
The ECML/PKDD Discovery Challenge 2016
on Bank Card Usage Analysis
Andrey Zimovnov Evgeny Sokolov
“Ya” team
Solu;on of ECML/PKDD 2016 Discovery Challenge
Task 2 “Upselling predic;on”, team “Peter”

2. Approach
User informa;on
User transac;ons
Feature vector:
• Personal (15)
• Cards & Wealth (8)
• Ac;veness (8)
• Event counters (77)
• Geo (28)
Predict target
(credit card in the future)
using XGBoost

3. Features: User informa;on
Personal features
• Gender
• Age category
• Loca;on category
• Income category
One-hot & label encoded
Card & Wealth features
• Number of months labeled as “1”
• Number of label changes “0” -> “1”
and vice versa
• Last month labeled “1”

4. Features: Event counters
Categorical features in user transac8ons:
1. Type of ac;vity (point of sale, webshop, branch)
2. Rounded ;me period (morning, day;me, evening)
3. Loca;on type (capital, city, village)
4. Marker category of payment (7 anonymous categories)
5. Type of card (credit, debit)
6. Amount of money (low, medium, high)
Two features for each category value:
• Number of events
integer
• Ra;o of events with par;cular value
in all user events
ra)onal in [0, 1]

5. Features: Ac;veness
Ac8ve = at least one transac;on during the
period (days and weeks)
Features:
• Number of ac;ve / inac;ve periods
• Ra;o of ac;ve periods

6. Features: Geo loca;on
Aggrega8on of transac8on geo-features:
• min, max
• mean, std
• percen;les (20%, 50%, 80%)
Geo-features of one transac8on:
• coordinates
• distance to the center (Budapest)
• horizontal angle of loca;on
wrt the center
dist
angle

7. Tuning learning parameters
• Valida;on: 10x stra;fied shuffle split on learning (90%) and valida;on (10%)
• Parameters to tune
– tree depth
– learning rate
– number of trees in ensemble
– scheme of filling the missing values
– number of unimportant features to exclude
• Decision
– Marginal improvement in valida;on score (about 0.005 with big variance)
– Biased valida;on scheme (because of year-to-year changes)
– Final submission: XGBoost model with default learning parameters (Occam’s
Razor principle)
100 trees, max depth = 3, learning rate = 0.1

8. Feature evalua;on
Feature vector:
• Personal (15)
• Cards & Wealth (8)
• Ac;veness (8)
• Event counters (77)
• Geo (28)
Feature group AUC change
aCer removing feature group
AUC
only features from the group
Personal -0.0322 0.6615
Cards & Wealth -0.0137 0.5653
Event counters -0.0019 0.6738
Ac;veness -0.0012 0.6419
Geo loca;on -0.0004 0.6318
Cross-valida;on AUC score: 0.7213 (stra;fied shuffle train/test split)

9. That’s it!
Solu8on source code:
hlps://github.com/romovpa/ecmlpkdd2016-otp-bank-upselling